INTELLIGENT SYSTEM FOR MONITORING THE TECHNICAL CONDITION OF AN ABOVEGROUND PIPELINE

Abstract

The paper considers modern approaches to monitoring the technical condition of main aboveground pipelines using intelligent diagnostic systems. An analysis of the main types of pipeline defects was carried out, including corrosion damage, cracks, mechanical deformations, weld defects, and damage to the insulation layer. It was established that the development of such defects leads to pipeline leakage and emergency situations with significant environmental and operational consequences. Modern methods of non-destructive pipeline testing are considered, including electromagnetic, ultrasonic, radiographic, acoustic, and surface diagnostic methods. Special attention is paid to the Smart Maintenance concept, which involves the integration of unmanned aerial vehicles (UAVs), IoT technologies, sensor systems, and machine learning algorithms for continuous monitoring of the technical condition of pipeline infrastructure. The prospects of applying computer vision methods, convolutional neural networks, and semantic segmentation systems for automated defect detection and classification are demonstrated. The main advantages of intelligent monitoring systems are identified, including improved diagnostic accuracy, reduced human factor influence, and optimization of pipeline maintenance processes.